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- W2772286466 abstract "The use of electroencephalogram (EEG) data is common to develop brain-computer interface (BCI) applications. Analysis of EEG data in the oddball paradigm has revealed that some electrodes experience clearer manifestations of the P300 wave, giving a particular relevance to their position. For this study, we arrange recorded EEG data as a single trial 3D representation in which spatial and temporal information are kept with few to no modifications for off-line classification. This differs from most studies using EEG data and convolutional neural networks (CNN), for which a 2D-mapped version of the data is normally used. The recorded data used for this study corresponds to the EEG of four subjects that were presented with auditory stimuli via earphones from six different virtual directions following the oddball paradigm to elicit the P300 wave. The stimuli presentation was done at time intervals of 500, 400, and 300 ms to verify the impact of the speed in the performance of an auditory BCI. The results obtained for the identification of P300 within the EEG shows that the 3D input proposed in this study is a reliable approach for input configuration and that the CNN models trained using it tend to correctly differentiate the EEG containing the P300 from that which doesn't with low accuracy variations regardless of the differences of the used models." @default.
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- W2772286466 date "2017-10-01" @default.
- W2772286466 modified "2023-09-26" @default.
- W2772286466 title "Single trial P300 identification for an auditory BCI: Implementation of a 3D input for convolutional neural networks" @default.
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- W2772286466 doi "https://doi.org/10.1109/smc.2017.8122607" @default.
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